Transcript 7class - Southern Miss School of Computing Moodle

Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 8 —
1
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
2
What is Classification




A bank loans officer needs analysis of her data to
learn which loan applicants are “safe” and which are
“risky” for the bank.
A marketing manager at AllElectronics needs data
analysis to help guess whether a customer with a
given profile will buy a new computer. (Yes/No)
A medical researcher wants to analyze breast cancer
data to predict which one of three specific
treatments a patient should receive. (A/B/C)
In each of these examples, the data analysis task is
classification, where a model or classifier is
constructed to predict class (categorical) labels,
3
What is Prediction



Suppose that the marketing manager wants to
predict how much a given customer will spend during
a sale at AllElectronics.
This data analysis task is an example of numeric
prediction, where the model constructed predicts a
continuous-valued function, or ordered value, as
opposed to a class label.
This model is a predictor. Regression analysis is a
statistical methodology that is most often used for
numeric prediction
4
Classification—A Two-Step Process


Model construction: describing a set of predetermined classes
 Each tuple/sample is assumed to belong to a predefined class, as
determined by the class label attribute
 The set of tuples used for model construction is training set
 The model is represented as classification rules, decision trees, or
mathematical formulae
Model usage: for classifying future or unknown objects
 Estimate accuracy of the model
 The known label of test sample is compared with the classified
result from the model
 Accuracy rate is the percentage of test set samples that are
correctly classified by the model
 Test set is independent of training set (otherwise overfitting)
 If the accuracy is acceptable, use the model to classify data tuples
whose class labels are not known
5
Learning and model construction
6
Terminology




Training dataset
Attribute vector
Class label attribute
Training sample/example/instance/object
7
Test and Classification

Classification: Test data are used to estimate the accuracy of the
classification rules. If the accuracy is considered acceptable, the rules can be
applied to the classification of new data tuples.
8
Terminology




Test dataset
Test samples
Accuracy of the model
Overfit (optimistic estimation of accuracy)
9
Process (1): Model Construction
Classification
Algorithms
Training
Data
NAME RANK
M ike
M ary
B ill
Jim
D ave
Anne
A ssistan t P ro f
A ssistan t P ro f
P ro fesso r
A sso ciate P ro f
A ssistan t P ro f
A sso ciate P ro f
YEARS TENURED
3
7
2
7
6
3
no
yes
yes
yes
no
no
Classifier
(Model)
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’
10
Process (2): Using the Model in Prediction
Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
NAME
Tom
M erlisa
G eorge
Joseph
RANK
Y E A R S TE N U R E D
A ssistant P rof
2
no
A ssociate P rof
7
no
P rofessor
5
yes
A ssistant P rof
7
yes
Tenured?
11
Supervised vs. Unsupervised Learning

Supervised learning (classification)

Supervision: The training data (observations,
measurements, etc.) are accompanied by labels indicating
the class of the observations


New data is classified based on the training set
Unsupervised learning (clustering)

The class labels of training data is unknown

Given a set of measurements, observations, etc. with the
aim of establishing the existence of classes or clusters in
the data
12
Prediction Problems: Classification vs.
Numeric Prediction



Classification
 predicts categorical class labels (discrete or nominal)
 classifies data (constructs a model) based on the training
set and the values (class labels) in a classifying attribute
and uses it in classifying new data
Numeric Prediction
 models continuous-valued functions, i.e., predicts
unknown or missing values
Typical applications
 Credit/loan approval:
 Medical diagnosis: if a tumor is cancerous or benign
 Fraud detection: if a transaction is fraudulent
 Web page categorization: which category it is
13
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
14
Decision Tree
15
Terminology






Decision tree induction is the learning of decision
trees from class-labeled training tuples.
A decision tree is a flowchart-like tree structure,
where each internal node (nonleaf node) denotes a
test on an attribute,
Each branch represents an outcome of the test,
and each leaf node (or terminal node) holds a class
label.
The topmost node in a tree is the root node.
16
Decision Tree Induction: An Example
Training data set: Buys_computer
 The data set follows an example of
Quinlan’s ID3 (Playing Tennis)
 Resulting tree:
age?

<=30
31..40
overcast
student?
no
no
yes
yes
yes
>40
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income student credit_rating buys_computer
high
no fair
no
high
no excellent
no
high
no fair
yes
medium
no fair
yes
low
yes fair
yes
low
yes excellent
no
low
yes excellent
yes
medium
no fair
no
low
yes fair
yes
medium yes fair
yes
medium yes excellent
yes
medium
no excellent
yes
high
yes fair
yes
medium
no excellent
no
credit rating?
excellent
fair
yes
17
Why decision tree



The construction of decision tree classifiers does not require
any domain knowledge or parameter setting, and therefore is
appropriate for exploratory knowledge discovery.
Decision trees can handle multidimensional data. Their
representation of acquired knowledge in tree form is intuitive
and generally easy to assimilate by humans.
The learning and classification steps of decision tree induction
are simple and fast. In general, decision tree classifiers have
good accuracy. However, successful use may depend on the
data at hand. Decision tree induction algorithms have been
used for classification in many application areas such as
medicine, manufacturing and production, financial analysis,
astronomy, and molecular biology. Decision trees are the basis
of several commercial rule induction systems.
18
Concepts in leaning decision tree



Attribute selection measures are used to select the attribute
that best partitions the tuples into distinct classes.
When decision trees are built, many of the branches may reflect
noise or outliers in the training data. Tree pruning attempts to
identify and remove such branches, with the goal of improving
classification accuracy on unseen data.
Scalability is a big issues for the induction of decision trees from
large databases
19
Tree algorithms



ID3 (Iterative Dichotomiser): J. Ross Quinlan, a researcher in
machine learning, developed a decision tree algorithm
C4.5(a successor of ID3)
CART(Classification and Regression Trees )
20
Algorithm for Decision Tree Induction


Basic algorithm (a greedy algorithm)
 Tree is constructed in a top-down recursive divide-andconquer manner
 At start, all the training examples are at the root
 Attributes are categorical (if continuous-valued, they are
discretized in advance)
 Examples are partitioned recursively based on selected
attributes
 Test attributes are selected on the basis of a heuristic or
statistical measure (e.g., information gain)
Conditions for stopping partitioning
 All samples for a given node belong to the same class
 There are no remaining attributes for further partitioning –
majority voting is employed for classifying the leaf
 There are no samples left
21
Attribute Selection Measure:
Information Gain (ID3/C4.5)

Select the attribute with the highest information gain

Let pi be the probability that an arbitrary tuple in D belongs to
class Ci, estimated by |Ci, D|/|D|

Expected information (entropy) needed to classify a tuple in D:
m
Info( D)   pi log2 ( pi )
i 1


Information needed (after using A to split D into v partitions) to
v
classify D:
| Dj |
InfoA ( D)  
 Info( D j )
j 1 | D |
Information gained by branching on attribute A
Gain(A) Info(D) InfoA(D)
22
Attribute Selection: Information Gain
Class P: buys_computer = “yes”
Class N: buys_computer = “no”


Info ( D)  I (9,5)  
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
9
9
5
5
log 2 ( )  log 2 ( ) 0.940
14
14 14
14
income student credit_rating
high
no
fair
high
no
excellent
high
no
fair
medium
no
fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no
fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no
excellent
high
yes fair
medium
no
excellent
buys_computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no
23
Attribute Selection: Information Gain


Class P: buys_computer = “yes”
Class N: buys_computer = “no”
Info ( D)  I (9,5)  
age
<=30
31…40
>40
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
Infoage ( D ) 
9
9
5
5
log 2 ( )  log 2 ( ) 0.940
14
14 14
14
pi
2
4
3
ni I(pi, ni)
3 0.971
0 0
2 0.971
income student credit_rating
high
no
fair
high
no
excellent
high
no
fair
medium
no
fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no
fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no
excellent
high
yes fair
medium
no
excellent
buys_computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no

5
4
I ( 2,3) 
I ( 4,0)
14
14
5
I (3,2)  0.694
14
5
I ( 2,3) means “age <=30” has 5 out of
14
14 samples, with 2 yes’es and 3
no’s.
24
Attribute Selection: Information Gain


Class P: buys_computer = “yes”
Class N: buys_computer = “no”
Info ( D)  I (9,5)  
age
<=30
31…40
>40
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
Infoage ( D ) 
9
9
5
5
log 2 ( )  log 2 ( ) 0.940
14
14 14
14
pi
2
4
3
ni I(pi, ni)
3 0.971
0 0
2 0.971
income student credit_rating
high
no
fair
high
no
excellent
high
no
fair
medium
no
fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no
fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no
excellent
high
yes fair
medium
no
excellent
buys_computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no

5
4
I ( 2,3) 
I ( 4,0)
14
14
5
I (3,2)  0.694
14
Gain(age)  Info(D)  Infoage (D)  0.246
Gain(income)  0.029
Gain( student )  0.151
Gain(credit _ rating )  0.048
25
26
m
Info( D)   pi log2 ( pi )
i 1
v
| Dj |
j 1
|D|
InfoA ( D)  
 Info( D j )
Gain(A) Info(D) InfoA(D)
27

Conditions for stopping
partitioning
 All samples for a given node
belong to the same class
 There are no remaining
attributes for further
partitioning – majority voting
is employed for classifying the
leaf
 There are no samples left
28
m
Info( D)   pi log2 ( pi )
i 1
v
| Dj |
j 1
|D|
InfoA ( D)  
 Info( D j )
Gain(A) Info(D) InfoA(D)
29
Computing Information-Gain for
Continuous-Valued Attributes

Let attribute A be a continuous-valued attribute

Must determine the best split point for A

Sort the value A in increasing order

Typically, the midpoint between each pair of adjacent values
is considered as a possible split point



(ai+ai+1)/2 is the midpoint between the values of ai and ai+1
The point with the minimum expected information
requirement for A is selected as the split-point for A
Split:

D1 is the set of tuples in D satisfying A ≤ split-point, and D2 is
the set of tuples in D satisfying A > split-point
30
31
Gain Ratio for Attribute Selection (C4.5)


Information gain measure is biased towards attributes with a
large number of values
C4.5 (a successor of ID3) uses gain ratio to overcome the
problem (normalization to information gain)
v
SplitInfoA ( D)  
j 1

| Dj |
| D|
 log2 (
| Dj |
| D|
)
GainRatio(A) = Gain(A)/SplitInfo(A)

Ex.

gain_ratio(income) = 0.029/1.557 = 0.019
The attribute with the maximum gain ratio is selected as the
splitting attribute

32
Gini Index (CART, IBM IntelligentMiner)

If a data set D contains examples from n classes, gini index,
n
gini(D) is defined as
gini ( D) 1
p2

j 1

j
where pj is the relative frequency of class j in D
If a data set D is split on A into two subsets D1 and D2, the gini
index gini(D) is defined as
|D |
|D |
gini A ( D) 
1
|D|
gini ( D1) 
2
|D|
gini ( D 2)

Reduction in Impurity:

The attribute provides the smallest ginisplit(D) (or the largest
reduction in impurity) is chosen to split the node (need to
enumerate all the possible splitting points for each attribute)
gini( A)  gini(D)  giniA (D)
33
Computation of Gini Index


Ex. D has 9 tuples in buys_computer = “yes”
and
5 in “no”
2
2
9 5
gini( D)  1        0.459
 14   14 
Suppose the attribute income partitions D into 10 in D1: {low,
medium} and 4 in D2 giniincome{low,medium} ( D)   10 Gini( D1 )   4 Gini( D1 )
 14 



 14 
Gini{low,high} is 0.458; Gini{medium,high} is 0.450. Thus, split on the
{low,medium} (and {high}) since it has the lowest Gini index
All attributes are assumed continuous-valued
May need other tools, e.g., clustering, to get the possible split
values
Can be modified for categorical attributes
34
Comparing Attribute Selection Measures

The three measures, in general, return good results but

Information gain:


Gain ratio:


biased towards multivalued attributes
tends to prefer unbalanced splits in which one partition is
much smaller than the others
Gini index:

biased to multivalued attributes

has difficulty when # of classes is large

tends to favor tests that result in equal-sized partitions
and purity in both partitions
35
Other Attribute Selection Measures

CHAID: a popular decision tree algorithm, measure based on χ2 test for
independence

C-SEP: performs better than info. gain and gini index in certain cases

G-statistic: has a close approximation to χ2 distribution

MDL (Minimal Description Length) principle (i.e., the simplest solution is
preferred):

The best tree as the one that requires the fewest # of bits to both (1)
encode the tree, and (2) encode the exceptions to the tree

Multivariate splits (partition based on multiple variable combinations)


CART: finds multivariate splits based on a linear comb. of attrs.
Which attribute selection measure is the best?

Most give good results, none is significantly superior than others
36
37
Overfitting and Tree Pruning


Overfitting: An induced tree may overfit the training data
 Too many branches, some may reflect anomalies due to
noise or outliers
 Poor accuracy for unseen samples
Two approaches to avoid overfitting
 Prepruning: Halt tree construction early ̵ do not split a node
if this would result in the goodness measure falling below a
threshold
 Difficult to choose an appropriate threshold
 Postpruning: Remove branches from a “fully grown” tree—
get a sequence of progressively pruned trees
 Use a set of data different from the training data to
decide which is the “best pruned tree”
38
Enhancements to Basic Decision Tree Induction

Allow for continuous-valued attributes



Dynamically define new discrete-valued attributes that
partition the continuous attribute value into a discrete set of
intervals
Handle missing attribute values

Assign the most common value of the attribute

Assign probability to each of the possible values
Attribute construction

Create new attributes based on existing ones that are
sparsely represented

This reduces fragmentation, repetition, and replication
39
Classification in Large Databases

Classification—a classical problem extensively studied by
statisticians and machine learning researchers

Scalability: Classifying data sets with millions of examples and
hundreds of attributes with reasonable speed

Why is decision tree induction popular?
 relatively faster learning speed (than other classification
methods)
 convertible to simple and easy to understand classification
rules
 can use SQL queries for accessing databases
 comparable classification accuracy with other methods
RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti)
 Builds an AVC-list (attribute, value, class label)

40
Scalability Framework for RainForest

Separates the scalability aspects from the criteria that
determine the quality of the tree

Builds an AVC-list: AVC (Attribute, Value, Class_label)

AVC-set (of an attribute X )

Projection of training dataset onto the attribute X and
class label where counts of individual class label are
aggregated

AVC-group (of a node n )

Set of AVC-sets of all predictor attributes at the node n
41
Rainforest: Training Set and Its AVC Sets
Training Examples
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
AVC-set on Age
income studentcredit_rating
buys_computerAge Buy_Computer
high
no fair
no
yes
no
high
no excellent no
<=30
2
3
high
no fair
yes
31..40
4
0
medium
no fair
yes
>40
3
2
low
yes fair
yes
low
yes excellent no
low
yes excellent yes
AVC-set on Student
medium
no fair
no
low
yes fair
yes
student
Buy_Computer
medium yes fair
yes
yes
no
medium yes excellent yes
medium
no excellent yes
yes
6
1
high
yes fair
yes
no
3
4
medium
no excellent no
AVC-set on income
income
Buy_Computer
yes
no
high
2
2
medium
4
2
low
3
1
AVC-set on
credit_rating
Buy_Computer
Credit
rating
yes
no
fair
6
2
excellent
3
3
42
That is All for today!
See you next week!
47
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
48
Bayesian Classification: Why?

A statistical classifier: performs probabilistic prediction, i.e.,
predicts class membership probabilities

Foundation: Based on Bayes’ Theorem.

Performance: A simple Bayesian classifier, naïve Bayesian
classifier, has comparable performance with decision tree and
selected neural network classifiers

Incremental: Each training example can incrementally
increase/decrease the probability that a hypothesis is correct —
prior knowledge can be combined with observed data

Standard: Even when Bayesian methods are computationally
intractable, they can provide a standard of optimal decision
making against which other methods can be measured
49
Bayesian Theorem: Basics






Let X be a data sample (“evidence”): class label is unknown
Let H be a hypothesis that X belongs to class C
Classification is to determine P(H|X), (posteriori probability), the
probability that the hypothesis holds given the observed data
sample X
P(H) (prior probability), the initial probability
 E.g., X will buy computer, regardless of age, income, …
P(X): probability that sample data is observed
P(X|H) (likelyhood), the probability of observing the sample X,
given that the hypothesis holds
 E.g., Given that X will buy computer, the prob. that X is
31..40, medium income
50
Bayesian Theorem

Given training data X, posteriori probability of a hypothesis H,
P(H|X), follows the Bayes theorem
P(H | X)  P(X | H )P(H )  P(X | H ) P(H ) / P(X)
P(X)

Informally, this can be written as
posteriori = likelihood x prior/evidence

Predicts X belongs to C2 iff the probability P(Ci|X) is the highest
among all the P(Ck|X) for all the k classes

Practical difficulty: require initial knowledge of many
probabilities, significant computational cost
51
Towards Naïve Bayesian Classifier




Let D be a training set of tuples and their associated class
labels, and each tuple is represented by an n-D attribute vector
X = (x1, x2, …, xn)
Suppose there are m classes C1, C2, …, Cm.
Classification is to derive the maximum posteriori, i.e., the
maximal P(Ci|X)
This can be derived from Bayes’ theorem
P(X | C )P(C )
i
i
P(C | X) 
i
P(X)

Since P(X) is constant for all classes, only
needs to be maximized
P(C | X)  P(X | C )P(C )
i
i
i
52
Derivation of Naïve Bayes Classifier

A simplified assumption: attributes are conditionally
independent (i.e., no dependence relation between
n
attributes):
P( X | C i)   P( x | C i)  P( x | C i)  P( x | C i)  ... P( x | C i)
k
1
2
n
k 1



This greatly reduces the computation cost: Only counts the
class distribution
If Ak is categorical, P(xk|Ci) is the # of tuples in Ci having value xk
for Ak divided by |Ci, D| (# of tuples of Ci in D)
If Ak is continous-valued, P(xk|Ci) is usually computed based on
Gaussian distribution with a mean μ and standard deviation σ
and P(xk|Ci) is
g ( x,  ,  ) 
1
e
2 

( x )2
2 2
P(X | Ci)  g ( xk , Ci , Ci )
53
Naïve Bayesian Classifier: Training Dataset
Class:
C1:buys_computer = ‘yes’
C2:buys_computer = ‘no’
Data sample
X = (age <=30,
Income = medium,
Student = yes
Credit_rating = Fair)
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income studentcredit_rating
buys_compu
high
no fair
no
high
no excellent
no
high
no fair
yes
medium no fair
yes
low
yes fair
yes
low
yes excellent
no
low
yes excellent yes
medium no fair
no
low
yes fair
yes
medium yes fair
yes
medium yes excellent yes
medium no excellent yes
high
yes fair
yes
medium no excellent
no
54
Naïve Bayesian Classifier: An Example
P(Ci): P(buys_computer = “yes”) = 9/14 = 0.643
P(buys_computer = “no”) = 5/14= 0.357

Compute P(X|Ci) for each class
P(age = “<=30” | buys_computer = “yes”) = 2/9 = 0.222
P(age = “<= 30” | buys_computer = “no”) = 3/5 = 0.6
P(income = “medium” | buys_computer = “yes”) = 4/9 = 0.444
P(income = “medium” | buys_computer = “no”) = 2/5 = 0.4
P(student = “yes” | buys_computer = “yes) = 6/9 = 0.667
P(student = “yes” | buys_computer = “no”) = 1/5 = 0.2
P(credit_rating = “fair” | buys_computer = “yes”) = 6/9 = 0.667
P(credit_rating = “fair” | buys_computer = “no”) = 2/5 = 0.4

X = (age <= 30 , income = medium, student = yes, credit_rating = fair)
P(X|Ci) : P(X|buys_computer = “yes”) = 0.222 x 0.444 x 0.667 x 0.667 = 0.044
P(X|buys_computer = “no”) = 0.6 x 0.4 x 0.2 x 0.4 = 0.019
P(X|Ci)*P(Ci) : P(X|buys_computer = “yes”) * P(buys_computer = “yes”) = 0.028
P(X|buys_computer = “no”) * P(buys_computer = “no”) = 0.007
Therefore, X belongs to class (“buys_computer = yes”)

55
Avoiding the Zero-Probability Problem

Naïve Bayesian prediction requires each conditional prob. be
non-zero. Otherwise, the predicted prob. will be zero
P( X | C i)



n
 P( x k | C i)
k 1
Ex. Suppose a dataset with 1000 tuples, income=low (0),
income= medium (990), and income = high (10)
Use Laplacian correction (or Laplacian estimator)
 Adding 1 to each case
Prob(income = low) = 1/1003
Prob(income = medium) = 991/1003
Prob(income = high) = 11/1003
 The “corrected” prob. estimates are close to their
“uncorrected” counterparts
56
Naïve Bayesian Classifier: Comments



Advantages
 Easy to implement
 Good results obtained in most of the cases
Disadvantages
 Assumption: class conditional independence, therefore loss
of accuracy
 Practically, dependencies exist among variables
 E.g., hospitals: patients: Profile: age, family history, etc.
Symptoms: fever, cough etc., Disease: lung cancer,
diabetes, etc.
 Dependencies among these cannot be modeled by Naïve
Bayesian Classifier
How to deal with these dependencies? Bayesian Belief Networks
(Chapter 9)
57
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
58
Using IF-THEN Rules for Classification



Represent the knowledge in the form of IF-THEN rules
R: IF age = youth AND student = yes THEN buys_computer = yes
 Rule antecedent/precondition vs. rule consequent
Assessment of a rule: coverage and accuracy
 ncovers = # of tuples covered by R
 ncorrect = # of tuples correctly classified by R
coverage(R) = ncovers /|D| /* D: training data set */
accuracy(R) = ncorrect / ncovers
If more than one rule are triggered, need conflict resolution
 Size ordering: assign the highest priority to the triggering rules that has
the “toughest” requirement (i.e., with the most attribute tests)
 Class-based ordering: decreasing order of prevalence or misclassification
cost per class
 Rule-based ordering (decision list): rules are organized into one long
priority list, according to some measure of rule quality or by experts
59
Rule Extraction from a Decision Tree
age?
<=30
31..40
>40

Rules are easier to understand than large treesstudent?
One rule is created for each path from the root
no
yes
to a leaf
no
yes
Each attribute-value pair along a path forms a
conjunction: the leaf holds the class prediction
Rules are mutually exclusive and exhaustive

Example: Rule extraction from our buys_computer decision-tree



credit rating?
yes
excellent
fair
yes
IF age = young AND student = no
THEN buys_computer = no
IF age = young AND student = yes
THEN buys_computer = yes
IF age = mid-age
THEN buys_computer = yes
IF age = old AND credit_rating = excellent THEN buys_computer = no
IF age = old AND credit_rating = fair
THEN buys_computer = yes
60
Rule Induction: Sequential Covering Method





Sequential covering algorithm: Extracts rules directly from training
data
Typical sequential covering algorithms: FOIL, AQ, CN2, RIPPER
Rules are learned sequentially, each for a given class Ci will cover
many tuples of Ci but none (or few) of the tuples of other classes
Steps:
 Rules are learned one at a time
 Each time a rule is learned, the tuples covered by the rules are
removed
 The process repeats on the remaining tuples unless termination
condition, e.g., when no more training examples or when the
quality of a rule returned is below a user-specified threshold
Comp. w. decision-tree induction: learning a set of rules
simultaneously
61
Sequential Covering Algorithm
while (enough target tuples left)
generate a rule
remove positive target tuples satisfying this rule
Examples covered
by Rule 2
Examples covered
by Rule 1
Examples covered
by Rule 3
Positive
examples
62
Rule Generation

To generate a rule
while(true)
find the best predicate p
if foil-gain(p) > threshold then add p to current rule
else break
A3=1&&A1=2
A3=1&&A1=2
&&A8=5
A3=1
Positive
examples
Negative
examples
63
How to Learn-One-Rule?



Start with the most general rule possible: condition = empty
Adding new attributes by adopting a greedy depth-first strategy
 Picks the one that most improves the rule quality
Rule-Quality measures: consider both coverage and accuracy
 Foil-gain (in FOIL & RIPPER): assesses info_gain by extending
condition
pos'
pos
FOIL _ Gain  pos'(log2


pos' neg'
 log2
pos  neg
)
favors rules that have high accuracy and cover many positive tuples
Rule pruning based on an independent set of test tuples
FOIL_ Prune( R) 
pos  neg
pos  neg
Pos/neg are # of positive/negative tuples covered by R.
If FOIL_Prune is higher for the pruned version of R, prune R
64
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
65
Model Evaluation and Selection

Evaluation metrics: How can we measure accuracy? Other
metrics to consider?

Use test set of class-labeled tuples instead of training set when
assessing accuracy

Methods for estimating a classifier’s accuracy:


Holdout method, random subsampling

Cross-validation

Bootstrap
Comparing classifiers:

Confidence intervals

Cost-benefit analysis and ROC Curves
66
Classifier Evaluation Metrics: Confusion
Matrix
Confusion Matrix:
Actual class\Predicted class
C1
¬ C1
C1
True Positives (TP)
False Negatives (FN)
¬ C1
False Positives (FP)
True Negatives (TN)
Example of Confusion Matrix:
Actual class\Predicted buy_computer buy_computer
class
= yes
= no


Total
buy_computer = yes
6954
46
7000
buy_computer = no
412
2588
3000
Total
7366
2634
10000
Given m classes, an entry, CMi,j in a confusion matrix indicates
# of tuples in class i that were labeled by the classifier as class j
May have extra rows/columns to provide totals
67
Classifier Evaluation Metrics: Accuracy,
Error Rate, Sensitivity and Specificity
A\P


C
¬C
Class Imbalance Problem:
C TP FN P
 One class may be rare, e.g.
¬C FP TN N
fraud, or HIV-positive
P’ N’ All
 Significant majority of the
negative class and minority of
Classifier Accuracy, or
the positive class
recognition rate: percentage of
test set tuples that are correctly  Sensitivity: True Positive
classified
recognition rate
Accuracy = (TP + TN)/All
 Sensitivity = TP/P
Error rate: 1 – accuracy, or
 Specificity: True Negative
recognition rate
Error rate = (FP + FN)/All
 Specificity = TN/N

68
Classifier Evaluation Metrics:
Precision and Recall, and F-measures

Precision: exactness – what % of tuples that the classifier
labeled as positive are actually positive

Recall: completeness – what % of positive tuples did the
classifier label as positive?
Perfect score is 1.0
Inverse relationship between precision & recall
F measure (F1 or F-score): harmonic mean of precision and
recall,




Fß: weighted measure of precision and recall
 assigns ß times as much weight to recall as to precision
69
Classifier Evaluation Metrics: Example

Actual Class\Predicted class
cancer = yes
cancer = no
Total
Recognition(%)
cancer = yes
90
210
300
30.00 (sensitivity
cancer = no
140
9560
9700
98.56 (specificity)
Total
230
9770
10000
96.40 (accuracy)
Precision = 90/230 = 39.13%
Recall = 90/300 = 30.00%
70
Evaluating Classifier Accuracy:
Holdout & Cross-Validation Methods


Holdout method
 Given data is randomly partitioned into two independent sets
 Training set (e.g., 2/3) for model construction
 Test set (e.g., 1/3) for accuracy estimation
 Random sampling: a variation of holdout
 Repeat holdout k times, accuracy = avg. of the accuracies
obtained
Cross-validation (k-fold, where k = 10 is most popular)
 Randomly partition the data into k mutually exclusive subsets,
each approximately equal size
 At i-th iteration, use Di as test set and others as training set
 Leave-one-out: k folds where k = # of tuples, for small sized
data
 *Stratified cross-validation*: folds are stratified so that class
dist. in each fold is approx. the same as that in the initial data
71
Evaluating Classifier Accuracy: Bootstrap

Bootstrap

Works well with small data sets

Samples the given training tuples uniformly with replacement


i.e., each time a tuple is selected, it is equally likely to be selected
again and re-added to the training set
Several bootstrap methods, and a common one is .632 boostrap

A data set with d tuples is sampled d times, with replacement, resulting in
a training set of d samples. The data tuples that did not make it into the
training set end up forming the test set. About 63.2% of the original data
end up in the bootstrap, and the remaining 36.8% form the test set (since
(1 – 1/d)d ≈ e-1 = 0.368)

Repeat the sampling procedure k times, overall accuracy of the model:
72
Estimating Confidence Intervals:
Classifier Models M1 vs. M2

Suppose we have 2 classifiers, M1 and M2, which one is better?

Use 10-fold cross-validation to obtain

These mean error rates are just estimates of error on the true
and
population of future data cases

What if the difference between the 2 error rates is just
attributed to chance?

Use a test of statistical significance

Obtain confidence limits for our error estimates
73
Estimating Confidence Intervals:
Null Hypothesis

Perform 10-fold cross-validation

Assume samples follow a t distribution with k–1 degrees of
freedom (here, k=10)

Use t-test (or Student’s t-test)

Null Hypothesis: M1 & M2 are the same

If we can reject null hypothesis, then

we conclude that the difference between M1 & M2 is
statistically significant

Chose model with lower error rate
74
Estimating Confidence Intervals: t-test

If only 1 test set available: pairwise comparison




For ith round of 10-fold cross-validation, the same cross
partitioning is used to obtain err(M1)i and err(M2)i
Average over 10 rounds to get
and
t-test computes t-statistic with k-1 degrees of
freedom:
where
If two test sets available: use non-paired t-test
where
where k1 & k2 are # of cross-validation samples used for M1 & M2, resp.
75
Estimating Confidence Intervals:
Table for t-distribution



Symmetric
Significance level,
e.g., sig = 0.05 or
5% means M1 & M2
are significantly
different for 95% of
population
Confidence limit, z
= sig/2
76
Estimating Confidence Intervals:
Statistical Significance

Are M1 & M2 significantly different?
 Compute t. Select significance level (e.g. sig = 5%)
 Consult table for t-distribution: Find t value corresponding
to k-1 degrees of freedom (here, 9)
 t-distribution is symmetric: typically upper % points of
distribution shown → look up value for confidence limit
z=sig/2 (here, 0.025)
 If t > z or t < -z, then t value lies in rejection region:
 Reject null hypothesis that mean error rates of M1 & M2
are same
 Conclude: statistically significant difference between M1
& M2
 Otherwise, conclude that any difference is chance
77
Model Selection: ROC Curves






ROC (Receiver Operating
Characteristics) curves: for visual
comparison of classification models
Originated from signal detection theory
Shows the trade-off between the true
positive rate and the false positive rate
The area under the ROC curve is a
measure of the accuracy of the model
Rank the test tuples in decreasing
order: the one that is most likely to
belong to the positive class appears at
the top of the list
The closer to the diagonal line (i.e., the
closer the area is to 0.5), the less
accurate is the model




Vertical axis
represents the true
positive rate
Horizontal axis rep.
the false positive rate
The plot also shows a
diagonal line
A model with perfect
accuracy will have an
area of 1.0
78
Issues Affecting Model Selection

Accuracy


classifier accuracy: predicting class label
Speed

time to construct the model (training time)

time to use the model (classification/prediction time)

Robustness: handling noise and missing values

Scalability: efficiency in disk-resident databases

Interpretability


understanding and insight provided by the model
Other measures, e.g., goodness of rules, such as decision tree
size or compactness of classification rules
79
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
80
Ensemble Methods: Increasing the Accuracy


Ensemble methods
 Use a combination of models to increase accuracy
 Combine a series of k learned models, M1, M2, …, Mk, with
the aim of creating an improved model M*
Popular ensemble methods
 Bagging: averaging the prediction over a collection of
classifiers
 Boosting: weighted vote with a collection of classifiers
 Ensemble: combining a set of heterogeneous classifiers
81
Bagging: Boostrap Aggregation





Analogy: Diagnosis based on multiple doctors’ majority vote
Training
 Given a set D of d tuples, at each iteration i, a training set Di of d tuples
is sampled with replacement from D (i.e., bootstrap)
 A classifier model Mi is learned for each training set Di
Classification: classify an unknown sample X
 Each classifier Mi returns its class prediction
 The bagged classifier M* counts the votes and assigns the class with the
most votes to X
Prediction: can be applied to the prediction of continuous values by taking
the average value of each prediction for a given test tuple
Accuracy
 Often significantly better than a single classifier derived from D
 For noise data: not considerably worse, more robust
 Proved improved accuracy in prediction
82
Boosting




Analogy: Consult several doctors, based on a combination of
weighted diagnoses—weight assigned based on the previous
diagnosis accuracy
How boosting works?

Weights are assigned to each training tuple

A series of k classifiers is iteratively learned

After a classifier Mi is learned, the weights are updated to
allow the subsequent classifier, Mi+1, to pay more attention to
the training tuples that were misclassified by Mi

The final M* combines the votes of each individual classifier,
where the weight of each classifier's vote is a function of its
accuracy
Boosting algorithm can be extended for numeric prediction
Comparing with bagging: Boosting tends to have greater accuracy,
but it also risks overfitting the model to misclassified data
83
Adaboost (Freund and Schapire, 1997)




Given a set of d class-labeled tuples, (X1, y1), …, (Xd, yd)
Initially, all the weights of tuples are set the same (1/d)
Generate k classifiers in k rounds. At round i,

Tuples from D are sampled (with replacement) to form a training set
Di of the same size

Each tuple’s chance of being selected is based on its weight

A classification model Mi is derived from Di

Its error rate is calculated using Di as a test set

If a tuple is misclassified, its weight is increased, o.w. it is decreased
Error rate: err(Xj) is the misclassification error of tuple Xj. Classifier Mi
error rate is the sum of the weights of the misclassified tuples:
d
error( M i )   w j  err ( X j )
j

The weight of classifier Mi’s vote is
log
1  error( M i )
error( M i )
84
Random Forest (Breiman 2001)




Random Forest:
 Each classifier in the ensemble is a decision tree classifier and is
generated using a random selection of attributes at each node to
determine the split
 During classification, each tree votes and the most popular class is
returned
Two Methods to construct Random Forest:
 Forest-RI (random input selection): Randomly select, at each node, F
attributes as candidates for the split at the node. The CART methodology
is used to grow the trees to maximum size
 Forest-RC (random linear combinations): Creates new attributes (or
features) that are a linear combination of the existing attributes
(reduces the correlation between individual classifiers)
Comparable in accuracy to Adaboost, but more robust to errors and outliers
Insensitive to the number of attributes selected for consideration at each
split, and faster than bagging or boosting
85
Classification of Class-Imbalanced Data Sets




Class-imbalance problem: Rare positive example but numerous
negative ones, e.g., medical diagnosis, fraud, oil-spill, fault, etc.
Traditional methods assume a balanced distribution of classes
and equal error costs: not suitable for class-imbalanced data
Typical methods for imbalance data in 2-class classification:
 Oversampling: re-sampling of data from positive class
 Under-sampling: randomly eliminate tuples from negative
class
 Threshold-moving: moves the decision threshold, t, so that
the rare class tuples are easier to classify, and hence, less
chance of costly false negative errors
 Ensemble techniques: Ensemble multiple classifiers
introduced above
Still difficult for class imbalance problem on multiclass tasks
86
Chapter 8. Classification: Basic Concepts

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Model Evaluation and Selection

Techniques to Improve Classification Accuracy:
Ensemble Methods

Summary
87
Summary (I)

Classification is a form of data analysis that extracts models
describing important data classes.

Effective and scalable methods have been developed for decision
tree induction, Naive Bayesian classification, rule-based
classification, and many other classification methods.

Evaluation metrics include: accuracy, sensitivity, specificity,
precision, recall, F measure, and Fß measure.

Stratified k-fold cross-validation is recommended for accuracy
estimation. Bagging and boosting can be used to increase overall
accuracy by learning and combining a series of individual models.
88
Summary (II)

Significance tests and ROC curves are useful for model selection.

There have been numerous comparisons of the different
classification methods; the matter remains a research topic

No single method has been found to be superior over all others
for all data sets

Issues such as accuracy, training time, robustness, scalability,
and interpretability must be considered and can involve trade-
offs, further complicating the quest for an overall superior
method
89
Reference: Books on Classification













E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011
L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees.
Wadsworth International Group, 1984.
C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification, 2ed. John Wiley, 2001
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining,
Inference, and Prediction. Springer-Verlag, 2001
H. Liu and H. Motoda (eds.). Feature Extraction, Construction, and Selection: A Data Mining
Perspective. Kluwer Academic, 1998T. M. Mitchell. Machine Learning. McGraw Hill, 1997
S. Marsland. Machine Learning: An Algorithmic Perspective. Chapman and Hall/CRC, 2009.
J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993
J. W. Shavlik and T. G. Dietterich. Readings in Machine Learning. Morgan Kaufmann, 1990.
P. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, 2005.
S. M. Weiss and C. A. Kulikowski. Computer Systems that Learn: Classification and Prediction
Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan
Kaufman, 1991.
S. M. Weiss and N. Indurkhya. Predictive Data Mining. Morgan Kaufmann, 1997.
I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, 2ed.
Morgan Kaufmann, 2005.
90
Reference: Decision-Trees















M. Ankerst, C. Elsen, M. Ester, and H.-P. Kriegel. Visual classification: An interactive approach to decision tree
construction. KDD'99
C. Apte and S. Weiss. Data mining with decision trees and decision rules. Future Generation Computer Systems,
13, 1997
C. E. Brodley and P. E. Utgoff. Multivariate decision trees. Machine Learning, 19:45–77, 1995.
P. K. Chan and S. J. Stolfo. Learning arbiter and combiner trees from partitioned data for scaling machine
learning. KDD'95
U. M. Fayyad. Branching on attribute values in decision tree generation. AAAI’94
M. Mehta, R. Agrawal, and J. Rissanen. SLIQ : A fast scalable classifier for data mining. EDBT'96.
J. Gehrke, R. Ramakrishnan, and V. Ganti. Rainforest: A framework for fast decision tree construction of large
datasets. VLDB’98.
J. Gehrke, V. Gant, R. Ramakrishnan, and W.-Y. Loh, BOAT -- Optimistic Decision Tree Construction. SIGMOD'99.
S. K. Murthy, Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey, Data Mining and
Knowledge Discovery 2(4): 345-389, 1998
J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986
J. R. Quinlan and R. L. Rivest. Inferring decision trees using the minimum description length principle.
Information and Computation, 80:227–248, Mar. 1989
S. K. Murthy. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and
Knowledge Discovery, 2:345–389, 1998.
R. Rastogi and K. Shim. Public: A decision tree classifier that integrates building and pruning. VLDB’98.
J. Shafer, R. Agrawal, and M. Mehta. SPRINT : A scalable parallel classifier for data mining. VLDB’96
Y.-S. Shih. Families of splitting criteria for classification trees. Statistics and Computing, 9:309–315, 1999.
91
Reference: Neural Networks







C. M. Bishop, Neural Networks for Pattern Recognition. Oxford University
Press, 1995
Y. Chauvin and D. Rumelhart. Backpropagation: Theory, Architectures, and
Applications. Lawrence Erlbaum, 1995
J. W. Shavlik, R. J. Mooney, and G. G. Towell. Symbolic and neural learning
algorithms: An experimental comparison. Machine Learning, 6:111–144,
1991
S. Haykin. Neural Networks and Learning Machines. Prentice Hall, Saddle
River, NJ, 2008
J. Hertz, A. Krogh, and R. G. Palmer. Introduction to the Theory of Neural
Computation. Addison Wesley, 1991.
R. Hecht-Nielsen. Neurocomputing. Addison Wesley, 1990
B. D. Ripley. Pattern Recognition and Neural Networks. Cambridge University
Press, 1996
92
Reference: Support Vector Machines






C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern
Recognition. Data Mining and Knowledge Discovery, 2(2): 121-168, 1998
N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector
Machines and Other Kernel-Based Learning Methods. Cambridge Univ. Press,
2000.
H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. N. Vapnik. Support
vector regression machines, NIPS, 1997
J. C. Platt. Fast training of support vector machines using sequential minimal
optimization. In B. Schoelkopf, C. J. C. Burges, and A. Smola, editors,
Advances in Kernel Methods|Support Vector Learning, pages 185–208. MIT
Press, 1998
B. Schl¨okopf, P. L. Bartlett, A. Smola, and R. Williamson. Shrinking the tube:
A new support vector regression algorithm. NIPS, 1999.
H. Yu, J. Yang, and J. Han. Classifying large data sets using SVM with
hierarchical clusters. KDD'03.
93
Reference: Pattern-Based Classification








H. Cheng, X. Yan, J. Han, and C.-W. Hsu, Discriminative Frequent Pattern Analysis
for Effective Classification, ICDE'07
H. Cheng, X. Yan, J. Han, and P. S. Yu, Direct Discriminative Pattern Mining for
Effective Classification, ICDE'08
G. Cong, K.-L. Tan, A. K. H. Tung, and X. Xu. Mining top-k covering rule groups for
gene expression data. SIGMOD'05
G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and
differences. KDD'99
H. S. Kim, S. Kim, T. Weninger, J. Han, and T. Abdelzaher. NDPMine: Efficiently
mining discriminative numerical features for pattern-based classification.
ECMLPKDD'10
W. Li, J. Han, and J. Pei, CMAR: Accurate and Efficient Classification Based on
Multiple Class-Association Rules, ICDM'01
B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining.
KDD'98
J. Wang and G. Karypis. HARMONY: Efficiently mining the best rules for
classification. SDM'05
94
References: Rule Induction






P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3:261–283,
1989.
W. Cohen. Fast effective rule induction. ICML'95
S. L. Crawford. Extensions to the CART algorithm. Int. J. Man-Machine Studies, 31:197–
217, Aug. 1989
J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. ECML’93
P. Smyth and R. M. Goodman. An information theoretic approach to rule induction.
IEEE Trans. Knowledge and Data Engineering, 4:301–316, 1992.
X. Yin and J. Han. CPAR: Classification based on predictive association rules. SDM'03
95
References: K-NN & Case-Based Reasoning





A. Aamodt and E. Plazas. Case-based reasoning: Foundational
issues, methodological variations, and system approaches. AI
Comm., 7:39–52, 1994.
T. Cover and P. Hart. Nearest neighbor pattern classification.
IEEE Trans. Information Theory, 13:21–27, 1967
B. V. Dasarathy. Nearest Neighbor (NN) Norms: NN Pattern
Classication Techniques. IEEE Computer Society Press, 1991
J. L. Kolodner. Case-Based Reasoning. Morgan Kaufmann, 1993
A. Veloso, W. Meira, and M. Zaki. Lazy associative classification.
ICDM'06
96
References: Bayesian Method & Statistical Models











A. J. Dobson. An Introduction to Generalized Linear Models. Chapman & Hall, 1990.
D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The
combination of knowledge and statistical data. Machine Learning, 1995.
G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic
networks from data. Machine Learning, 9:309–347, 1992
A. Darwiche. Bayesian networks. Comm. ACM, 53:80–90, 2010
A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data
via the EM algorithm. J. Royal Statistical Society, Series B, 39:1–38, 1977
D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The
combination of knowledge and statistical data. Machine Learning, 20:197–243, 1995
F. V. Jensen. An Introduction to Bayesian Networks. Springer Verlag, 1996.
D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques.
The MIT Press, 2009
J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kauffman, 1988
S. Russell, J. Binder, D. Koller, and K. Kanazawa. Local learning in probabilistic networks
with hidden variables. IJCAI'95
V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, 1998.
97
Refs: Semi-Supervised & Multi-Class Learning






O. Chapelle, B. Schoelkopf, and A. Zien. Semi-supervised
Learning. MIT Press, 2006
T. G. Dietterich and G. Bakiri. Solving multiclass learning
problems via error-correcting output codes. J. Articial
Intelligence Research, 2:263–286, 1995
W. Dai, Q. Yang, G. Xue, and Y. Yu. Boosting for transfer
learning. ICML’07
S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Trans.
on Knowledge and Data Engineering, 22:1345–1359, 2010
B. Settles. Active learning literature survey. In Computer
Sciences Technical Report 1648, Univ. Wisconsin-Madison, 2010
X. Zhu. Semi-supervised learning literature survey. CS Tech. Rep.
1530, Univ. Wisconsin-Madison, 2005
98
Refs: Genetic Algorithms & Rough/Fuzzy Sets







D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine
Learning. Addison-Wesley, 1989
S. A. Harp, T. Samad, and A. Guha. Designing application-specific neural
networks using the genetic algorithm. NIPS, 1990
Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs.
Springer Verlag, 1992.
M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, 1996
Z. Pawlak. Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer
Academic, 1991
S. Pal and A. Skowron, editors, Fuzzy Sets, Rough Sets and Decision Making
Processes. New York, 1998
R. R. Yager and L. A. Zadeh. Fuzzy Sets, Neural Networks and Soft Computing.
Van Nostrand Reinhold, 1994
99
References: Model Evaluation, Ensemble Methods






L. Breiman. Bagging predictors. Machine Learning, 24:123–140, 1996.
L. Breiman. Random forests. Machine Learning, 45:5–32, 2001.
C. Elkan. The foundations of cost-sensitive learning. IJCAI'01
B. Efron and R. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall, 1993.
J. Friedman and E. P. Bogdan. Predictive learning via rule ensembles. Ann. Applied
Statistics, 2:916–954, 2008.
T.-S. Lim, W.-Y. Loh, and Y.-S. Shih. A comparison of prediction accuracy, complexity,
and training time of thirty-three old and new classification algorithms. Machine
Learning, 2000.

J. Magidson. The Chaid approach to segmentation modeling: Chi-squared automatic
interaction detection. In R. P. Bagozzi, editor, Advanced Methods of Marketing
Research, Blackwell Business, 1994.

J. R. Quinlan. Bagging, boosting, and c4.5. AAAI'96.
G. Seni and J. F. Elder. Ensemble Methods in Data Mining: Improving Accuracy
Through Combining Predictions. Morgan and Claypool, 2010.
Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning
and an application to boosting. J. Computer and System Sciences, 1997


100
Surplus Slides
101
Issues: Evaluating Classification Methods






Accuracy
 classifier accuracy: predicting class label
 predictor accuracy: guessing value of predicted attributes
Speed
 time to construct the model (training time)
 time to use the model (classification/prediction time)
Robustness: handling noise and missing values
Scalability: efficiency in disk-resident databases
Interpretability
 understanding and insight provided by the model
Other measures, e.g., goodness of rules, such as decision tree
size or compactness of classification rules
102
Gain Ratio for Attribute Selection (C4.5)
(MK:contains errors)

Information gain measure is biased towards attributes with a
large number of values

C4.5 (a successor of ID3) uses gain ratio to overcome the
problem (normalization to information gain)
v
SplitInfoA ( D)  
j 1


Ex.


| Dj |
| D|
 log2 (
| Dj |
| D|
)
GainRatio(A) = Gain(A)/SplitInfo(A)
SplitInfo A ( D)  
4
4
6
6
4
4
 log 2 ( )   log 2 ( )   log 2 ( )  0.926
14
14 14
14 14
14
gain_ratio(income) = 0.029/0.926 = 0.031
The attribute with the maximum gain ratio is selected as the
splitting attribute
103
Gini index (CART, IBM IntelligentMiner)

Ex. D has 9 tuples in buys_computer = “yes” and 5 in “no”
2

2
9 5
gini( D)  1        0.459
 14   14 
Suppose the attribute income partitions D into 10 in D1: {low, medium} and 4
 10 
4
in D2
gini
( D)   Gini( D )   Gini( D )
income{low, medium}
 14 
1
 14 
1
but gini{medium,high} is 0.30 and thus the best since it is the lowest

All attributes are assumed continuous-valued

May need other tools, e.g., clustering, to get the possible split values

Can be modified for categorical attributes
104
Predictor Error Measures

Measure predictor accuracy: measure how far off the predicted value is from
the actual known value

Loss function: measures the error betw. yi and the predicted value yi’


Absolute error: | yi – yi’|

Squared error: (yi – yi’)2
Test error (generalization error):
the average loss over the test set
d
d

Mean absolute error:
'|
 | y  yMean
squared error:
i
i 1
i
d
Relative absolute error:
y '|
 | y Relative
squared error:
i 1
d
i
| y
i 1
i 1
i
 yi ' ) 2
d
 ( yi  yi ' ) 2
d
d

(y
i
i
y|
The mean squared-error exaggerates the presence of outliers
i 1
d
(y
i 1
i
 y)2
Popularly use (square) root mean-square error, similarly, root relative
squared error
105
Scalable Decision Tree Induction Methods





SLIQ (EDBT’96 — Mehta et al.)
 Builds an index for each attribute and only class list and the
current attribute list reside in memory
SPRINT (VLDB’96 — J. Shafer et al.)
 Constructs an attribute list data structure
PUBLIC (VLDB’98 — Rastogi & Shim)
 Integrates tree splitting and tree pruning: stop growing the
tree earlier
RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti)
 Builds an AVC-list (attribute, value, class label)
BOAT (PODS’99 — Gehrke, Ganti, Ramakrishnan & Loh)
 Uses bootstrapping to create several small samples
106
Data Cube-Based Decision-Tree Induction

Integration of generalization with decision-tree induction
(Kamber et al.’97)

Classification at primitive concept levels


E.g., precise temperature, humidity, outlook, etc.

Low-level concepts, scattered classes, bushy classificationtrees

Semantic interpretation problems
Cube-based multi-level classification

Relevance analysis at multi-levels

Information-gain analysis with dimension + level
107